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Computational RNA secondary structure design: empirical complexity and improved methods.

Aguirre-Hernández R, Hoos HH, Condon A - BMC Bioinformatics (2007)

Bottom Line: Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure.Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada. rosalia@cs.ubc.ca <rosalia@cs.ubc.ca>

ABSTRACT

Background: We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.

Results: To gain insights into the practical complexity of the problem, we present a scaling analysis on random and biologically motivated structures using an improved version of the RNA-SSD algorithm, and also the RNAinverse algorithm from the Vienna package. Since primary structure constraints are relevant for designing RNA structures, we also investigate the correlation between the number and the location of the primary structure constraints when designing structures and the performance of the RNA-SSD algorithm. The scaling analysis on random and biologically motivated structures supports the hypothesis that the running time of both algorithms scales polynomially with the size of the structure. We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure. Furthermore, we prove that, according to the standard thermodynamic model, for some structures that the RNA-SSD algorithm was unable to design, there exists no sequence whose minimum free energy structure is the target structure.

Conclusion: Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

Show MeSH
Search cost distribution of RNAinverse. Distribution of expected run time of RNAinverse on (a) random structures and (b) biologically motivated structures. We report the expected run time for structures that RNAinverse is unable to design as 106 CPU seconds.
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Figure 4: Search cost distribution of RNAinverse. Distribution of expected run time of RNAinverse on (a) random structures and (b) biologically motivated structures. We report the expected run time for structures that RNAinverse is unable to design as 106 CPU seconds.

Mentions: As can be seen from Figure 2b, we obtained similar results for random structures as well as when using RNAinverse. Notice that overall, RNA-SSD performs substantially better than RNAinverse, and that random structures tend to be somewhat more difficult to design than biologically motivated structures. Distributions of expected run time for RNA-SSD over our sets of random and biologically motivated structures of various sizes are shown in Figures 3 and 4. Note that there is a large variation in difficulty between structures from the same set. Also, there are some structures that RNA-SSD is unable to design (the same holds for RNAinverse, as will be explained later).


Computational RNA secondary structure design: empirical complexity and improved methods.

Aguirre-Hernández R, Hoos HH, Condon A - BMC Bioinformatics (2007)

Search cost distribution of RNAinverse. Distribution of expected run time of RNAinverse on (a) random structures and (b) biologically motivated structures. We report the expected run time for structures that RNAinverse is unable to design as 106 CPU seconds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808480&req=5

Figure 4: Search cost distribution of RNAinverse. Distribution of expected run time of RNAinverse on (a) random structures and (b) biologically motivated structures. We report the expected run time for structures that RNAinverse is unable to design as 106 CPU seconds.
Mentions: As can be seen from Figure 2b, we obtained similar results for random structures as well as when using RNAinverse. Notice that overall, RNA-SSD performs substantially better than RNAinverse, and that random structures tend to be somewhat more difficult to design than biologically motivated structures. Distributions of expected run time for RNA-SSD over our sets of random and biologically motivated structures of various sizes are shown in Figures 3 and 4. Note that there is a large variation in difficulty between structures from the same set. Also, there are some structures that RNA-SSD is unable to design (the same holds for RNAinverse, as will be explained later).

Bottom Line: Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure.Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada. rosalia@cs.ubc.ca <rosalia@cs.ubc.ca>

ABSTRACT

Background: We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.

Results: To gain insights into the practical complexity of the problem, we present a scaling analysis on random and biologically motivated structures using an improved version of the RNA-SSD algorithm, and also the RNAinverse algorithm from the Vienna package. Since primary structure constraints are relevant for designing RNA structures, we also investigate the correlation between the number and the location of the primary structure constraints when designing structures and the performance of the RNA-SSD algorithm. The scaling analysis on random and biologically motivated structures supports the hypothesis that the running time of both algorithms scales polynomially with the size of the structure. We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure. Furthermore, we prove that, according to the standard thermodynamic model, for some structures that the RNA-SSD algorithm was unable to design, there exists no sequence whose minimum free energy structure is the target structure.

Conclusion: Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

Show MeSH